Data Structure Metrics in Software Engineering: Assessing Data Handling Complexity

Explore data structure metrics used in software engineering to quantify data usage and complexity. This guide explains key metrics (data structure complexity, number of live variables, etc.), their calculation, and how they help estimate development effort and assess the maintainability of software systems.

Data Structure Metrics in Software Engineering

Introduction to Data Structure Metrics

Software systems fundamentally process data: taking input, using it internally, and producing output. Data structure metrics provide a way to quantify the amount and complexity of data handling within a software system. These metrics focus on variables and constants within modules, ignoring input/output dependencies. Understanding these metrics can help estimate the effort and time required for software development.

Key Data Structure Metrics

Several metrics help assess data usage and complexity:

  1. The Amount of Data: This category includes metrics like:
    • Number of Variables (VARS): A simple count of variables used in the program.
    • Number of Operands (η₂): The total number of operands (variables, constants, and labels).
    • Total Occurrences of Variables (N₂): The total number of times each variable is used.
  2. Usage of Data within a Module: This metric calculates the average number of "live" variables in a module. A variable is considered "live" from its first use to its last use within a procedure.
  3. Program Weakness (WP): A measure of the overall weakness of a program, often related to the cohesion of its modules. Weaker modules (low cohesion) usually imply more complex code, leading to higher development effort.
  4. Data Sharing Among Modules: This metric reflects the coupling between modules. Higher coupling (more data sharing) typically means greater complexity and increased development effort due to increased communication between modules.

Formulas and Calculations

The average number of live variables (LV) across multiple modules can be calculated as:

LV = (Σi=1n LVi) / n

Where LVi is the average number of live variables in module i and n is the total number of modules.

Program weakness (WP) is calculated as:

WP = (Σi=1m WMi) / m

Where WMi = LVi * γ (γ is a constant representing the module's complexity), WMi is the weakness of the ith module, and m is the total number of modules.

Conclusion

Data structure metrics provide valuable insights into a software system's data handling characteristics. By analyzing these metrics, developers can better estimate development effort, identify potential problem areas, and improve software design. These metrics are especially useful during the planning and design phases of software development.